MétaCan
Menu
Back to cohort
Record W2063406109 · doi:10.4236/health.2013.53a084

Psychometric properties of the interRAI subjective quality of life Instrument for mental health

2013· article· en· W2063406109 on OpenAlexaffabout
Tess E. Naus, John P. Hirdes

Bibliographic record

VenueHealth · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMental healthCronbach's alphaQuality of life (healthcare)Reliability (semiconductor)Minimum Data SetMedicineQuality (philosophy)PsychologyNursingApplied psychologyPsychiatryPsychometricsClinical psychology

Abstract

fetched live from OpenAlex

A new Subjective Quality of Life (SQoL) Instrument for inpatient and community mental health settings was developed by the interRAI research collaborative to support evaluation of quality in mental health settings from the person’s perspective. Ratings of SQoL provide important information about the quality of service and patient experience with the care they receive. This information can help staff to improve approaches to each person’s plan of care in a manner that is meaningful to the individual. This study examined the reliability of the SQoL-MH. 83 inpatients from several clinical departments in a mental health center in South Western Ontario, Canada were randomly assigned to either be interviewed or complete the assessment on his or her own. Reliability was tested using Cronbach’s Alpha. A preliminary factor analysis points to four SQoL-MH subscales with very good internal consistency, ranging from 0.83 to 0.90. Once finalized, the Subjective Quality of Life instrument will be integral to the interRAI suite of instruments used to assess persons with mental health needs. A reliable and valid SQoL-MH instrument will allow mental health service providers to shape or modify care environments in order to enhance quality of life. In addition, the SQoL-MH instrument could also benefit advocacy groups who use reports on quality of life to influence social policy development and funding decisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.167
GPT teacher head0.444
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueHealthSame topicGeriatric Care and Nursing HomesFrench-language works237,207